Impact of Forest Coverage on Urban Air Quality: A Global Analysis¶

Author: Avsar Vora¶

Table of contents¶

  • Introduction
  • Dataset Overview
  • Data Load and Data Preparation
  • Forest Area vs Air Quality Analysis
  • Conclusion

Introduction¶

This research delves into the complex relationship between the forest areas of various countries and environmental factors, specifically focusing on Urban Air Quality metrics such as AQI, CO levels, and Ozone AQI, among others. Through comprehensive dataset analysis, the study seeks to identify patterns and correlations that highlight the broader impact of forest coverage on climate resilience across different nations.

The motivation for this research stems from the urgent need to understand the role of forest coverage in mitigating environmental challenges, particularly in urban areas where air quality issues are most pronounced. Forests play a crucial role in carbon sequestration, pollutant filtration, and overall climate regulation. By investigating the correlation between forest areas and urban air quality metrics, this study aims to provide valuable insights that can inform policy decisions, promote sustainable urban planning, and enhance climate resilience. Understanding these relationships is critical in the context of global environmental changes and the increasing pressure on urban ecosystems to adapt and thrive.

Goals¶

The main objective is to examine and illustrate the connections between:

  • World's Forest Area Ratio Change since 1992
  • Forest Area Ratio Change per Country
  • Top 10 Countries with Expanding Forest Area Ratio
  • Top 20 Countries with Most Forest Area Ratio (2021)
  • Comparision of contries Air Quality with Forest cover (2021)

Dataset Overview¶

Datasource1: World Forest Area¶

  • Metadata URL: https://www.kaggle.com/datasets/webdevbadger/world-forest-area/data
  • Data URL: https://www.kaggle.com/datasets/webdevbadger/world-forest-area/download
  • Data Source & Data Type: Kaggle - CSV
  • License: Creative Commons Attribution 4.0 International License.

The dataset comprises 34 columns, encompassing detailed information such as the country name, country code, and the percentage of forest area as a proportion of the total land area. The dataset spans from 1990 to 2021, with individual columns representing the annual percentage of forest area for each year within this period.

Datasource2: World Air Quality Index¶

  • Metadata URL: https://www.kaggle.com/datasets/adityaramachandran27/world-air-quality-index-by-city-and-coordinates/data
  • Data URL: https://www.kaggle.com/datasets/adityaramachandran27/world-air-quality-index-by-city-and-coordinates/download
  • Data Source & Data Type: Kaggle - CSV
  • License: Creative Commons Attribution 4.0 International License.

This dataset contains detailed information on various countries, including the number of cities, their geographic coordinates (latitude and longitude), and different air quality indices recorded in 2021.

Data Load and Preparation¶

Import Packages¶

In [17]:
import os
import subprocess

import matplotlib.ticker as mtick
import numpy as np
import pandas as pd
import plotly.express as px
import pycountry
import seaborn as sns
from matplotlib import pyplot as plt
from sqlalchemy import create_engine

plt.style.use('ggplot')
percent_formatter = mtick.StrMethodFormatter('{x:,.0f}%')
percent_d_formatter = mtick.StrMethodFormatter('{x:,.00f}%')

Data Load: Load data from sqlite database for Forest Area and Air Quality Index¶

In [6]:
# If database doesn't exist, run data_collection pipeline.
if not (os.path.exists("../data/processed/world_air_quality_data.sqlite") and os.path.exists(
        "../data/processed/world_forest_data.sqlite")):
    subprocess.run(["./data_collection.sh"])

# Create sqlite engine to make connection
air_quality_engine = create_engine(f"sqlite:///../data/processed/world_air_quality_data.sqlite")
forest_engine = create_engine(f"sqlite:///../data/processed/world_forest_data.sqlite")

# Load data from sqlite to pandas data frame
air_quality_df = pd.read_sql_table('world_air_quality_data', air_quality_engine)
forest_df = pd.read_sql_table('world_forest_data', forest_engine)
In [7]:
air_quality_df.head()
Out[7]:
Country AQI Value CO AQI Value Ozone AQI Value NO2 AQI Value PM2.5 AQI Value
0 Afghanistan 86.333333 0.333333 42.000000 0.000000 86.333333
1 Albania 77.111111 1.000000 42.555556 0.555556 76.555556
2 Algeria 106.250000 4.000000 35.000000 25.750000 106.250000
3 Andorra 32.000000 1.000000 32.000000 0.000000 24.000000
4 Angola 85.000000 3.380952 23.190476 2.238095 82.523810
In [8]:
forest_df.head()
Out[8]:
Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998 ... 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
0 Afghanistan 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 ... 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782 1.852782
1 Albania 28.788321 28.717153 28.645985 28.574818 28.503650 28.432482 28.361314 28.290146 28.218978 ... 28.646606 28.698558 28.750511 28.802464 28.802190 28.792062 28.791971 28.791971 28.791971 28.791971
2 Algeria 0.699908 0.696214 0.692519 0.688824 0.685129 0.681435 0.677740 0.674045 0.670350 ... 0.811675 0.814866 0.818057 0.821248 0.821248 0.815790 0.810332 0.814110 0.818309 0.822228
3 American Samoa 90.350000 90.180000 90.010000 89.840000 89.670000 89.500000 89.330000 89.160000 88.990000 ... 86.850000 86.700000 86.550000 86.400000 86.250000 86.100000 85.950000 85.800000 85.650000 85.500000
4 Andorra 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 ... 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553 34.042553

5 rows × 33 columns

Data Preparation: Prepare data frames for Analysis¶

In [9]:
# Filter world forest data for timeline visualization
world_df = forest_df[forest_df["Country Name"] == "World"].set_index("Country Name").transpose().dropna()

# Country vise grouping of data for visualization of Forest area ratio change per country
countries = []
for country in pycountry.countries:
    countries.append(country.name)
country_df = forest_df[forest_df["Country Name"].isin(countries)]
c_diff_df = country_df.set_index("Country Name").transpose().diff().sum().reset_index(name="change")

# Top 10 countries with expanding forest area data for visualization
top_p_change_df = c_diff_df.sort_values("change", ascending=False).head(10)

# Top 20 Countries with Most Forest Area Ratio (2021)
top_df = country_df.sort_values("2021", ascending=False).head(20)

# Combined data for heatmap visulization
df_2021 = country_df[["Country Name", "2021"]].rename(columns={"2021": "Forest Area"})
air_country_df = air_quality_df.groupby("Country")[
    ["AQI Value", "CO AQI Value", "Ozone AQI Value", "NO2 AQI Value", "PM2.5 AQI Value"]].mean().reset_index()
comb_df = pd.merge(df_2021, air_country_df, left_on="Country Name", right_on="Country").drop(columns="Country")

Forest Area vs Air Quality Analysis¶

World's Forest Area Ratio Change since 1992¶

The graph illustrates that the world's forest area ratio decreased by 1.2% from 1992 to 2021.

In [10]:
fig, ax = plt.subplots(figsize=(12, 5))
world_df.plot(colormap="Accent", ax=ax)
ax.yaxis.set_major_formatter(percent_d_formatter)
plt.title("World's Forest Area Ratio Change since 1992")
plt.legend("")
plt.show()
No description has been provided for this image

Forest Area Ratio Change per Country¶

The graph depicts the change in forest area ratio per country, highlighting the varying degrees of deforestation or reforestation across different nations. Notable trends and significant changes in forest coverage over the observed period are clearly visible.

In [11]:
ax = c_diff_df.sort_values("change").plot.barh(x="Country Name", y="change", figsize=(5, 32), colormap="Accent")
plt.title("Forest Area Ratio Change per Country (1980 - 2021)")
plt.legend("")
ax.xaxis.set_major_formatter(percent_formatter)
plt.show()
No description has been provided for this image

Top 10 Countries with Expanding Forest Area Ratio¶

The graph showcases the top 10 countries with the most significant increases in forest area ratio, emphasizing those nations that have successfully expanded their forest coverage. These positive trends highlight effective reforestation and conservation efforts.

In [12]:
fig = px.pie(values=top_p_change_df["change"], names=top_p_change_df["Country Name"],
             title="Top 10 Countries with Expanding Forest Area Ratio")
fig.update_traces(textposition='outside',
                  textinfo='percent+label',
                  marker=dict(line=dict(color='#FFFFFF', width=2)),
                  textfont_size=10)
fig.show()

Top 20 Countries with Most Forest Area Ratio (2021)¶

The graph presents the top 20 countries with the highest forest area ratio in 2021, highlighting those with the largest proportion of land covered by forests. These countries play a crucial role in global biodiversity conservation and climate regulation due to their extensive forested areas.

In [13]:
fig, ax = plt.subplots(figsize=(10, 5))
top_df[["Country Name", "2021"]].plot.bar(x="Country Name", y="2021", colormap='Accent', ax=ax)
plt.title("Countries with Most Forest Area Ratio (2021)")
plt.legend("")
ax.yaxis.set_major_formatter(percent_formatter)
plt.show()
No description has been provided for this image

Comparision of contries Air Quality with Forest cover (2021)¶

we are comparing with contries Air Quality using the World Air Quality Index by City and Coordinates dataset. Here we do see somewhat a significant correlation, especially with AQI, Ozone AQI, and PM2.5 AQI values.

In [18]:
plt.figure(figsize=(5, 5))
sns.heatmap(comb_df.select_dtypes(include=[np.number]).corr()[["Forest Area"]], annot=True, cmap='Greens')
plt.show()
No description has been provided for this image

Conclusion¶

The analysis of 2021 datasets reveals a significant correlation between forest cover and air quality across various countries. Nations with higher forest area ratios tend to exhibit better air quality metrics, underscoring the critical role of forests in enhancing urban air quality and overall environmental health. This finding highlights the importance of forest conservation and reforestation efforts in mitigating air pollution and promoting sustainable urban environments.